Job description

Tiger Analytics is looking for experienced Forward Deployed Engineer (Generative AI) with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. Role Overview The Forward Deployed Engineer (FDE) drives the on-site deployment, integration, and scaling of our enterprise Generative AI solutions. This role embeds directly within customer engineering teams to operationalize Large Language Models (LLMs) and retrieval systems across Google Cloud Platform(GCP). . You will bridge the gap between AI research and production-grade cloud infrastructure. You will collaborate with cross-functional teams and business partners and will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results. Technical Requirements • GCP & Vertex AI Architecture: Advanced knowledge of Vertex AI primitives , including Vertex AI Studio , Model Registry , Endpoint deployment , Vertex AI Pipelines (Kubeflow) , and Vertex AI Vector Search . • AI Frameworks: Hands-on experience with LLM orchestration tools (LangChain, LlamaIndex, AutoGen) and deep learning frameworks (PyTorch, Hugging Face) optimized for GCP infrastructure. • Vector Databases: Production experience setting up, optimizing, and querying Vertex AI Vector Search , or managed vector stores like Milvus , Pinecone , and pgvector (Cloud SQL/Spanner) . • Model Operations (LLMOps): Proficiency in model serving frameworks ( vLLM , TGI , Triton Inference Server ) deployed via Vertex AI or GKE, alongside robust automated model evaluation pipelines. • Containers & Kubernetes: Deep expertise in Google Kubernetes Engine (GKE) for managing GPU/TPU workloads , autoscaling, and scheduling. • IaC & Automation: Mastery of Terraform to provision secure, complex GCP environments, IAM roles, and Vertex AI resources. • Programming: Strong coding skills in Python (preferred) or Go , with an emphasis on writing clean, concurrent code and utilizing the Google Cloud SDK . Key Responsibilities- • AI Solution Deployment : Deploy, fine-tune, and optimize large-scale Gen AI models and LLM orchestration frameworks within customer cloud environments. • Infrastructure Engineering : Architect scalable infrastructure for AI workloads utilizing GPU/TPU orchestration, high-performance storage, and low-latency networking. • Data & Retrieval Pipelines : Design and implement high-throughput data ingestion pipelines and Vector Database architectures for Retrieval-Augmented Generation (RAG). • Technical Advocacy : Act as the primary technical consultant, guiding enterprise clients through AI safety, prompt engineering patterns, and inference cost optimization. • Product Collaboration : Feed edge-case deployment insights back to core AI research and platform engineering teams to improve product robustness. Soft Skills- • AI Consultation : Ability to manage customer expectations around LLM non-determinism, hallucinations, and performance trade-offs. • Rapid Adaptability : Passion for keeping pace with the weekly advancements in the Generative AI landscape. • Critical Debugging : Exceptional skill in isolating errors across complex software layers, from GPU drivers up to prompt engineering logic. • Mobility : Willingness to travel to client sites to lead high-stakes, on-site deployment sprints. This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility. Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

Requirements

Must-haves

We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.
Technical Requirements
GCP & Vertex AI Architecture: Advanced knowledge of Vertex AI primitives , including Vertex AI Studio , Model Registry , Endpoint deployment , Vertex AI Pipelines (Kubeflow) , and Vertex AI Vector Search .
AI Frameworks: Hands-on experience with LLM orchestration tools (LangChain, LlamaIndex, AutoGen) and deep learning frameworks (PyTorch, Hugging Face) optimized for GCP infrastructure.
Vector Databases: Production experience setting up, optimizing, and querying Vertex AI Vector Search , or managed vector stores like Milvus , Pinecone , and pgvector (Cloud SQL/Spanner) .
Model Operations (LLMOps): Proficiency in model serving frameworks ( vLLM , TGI , Triton Inference Server ) deployed via Vertex AI or GKE, alongside robust automated model evaluation pipelines.
Containers & Kubernetes: Deep expertise in Google Kubernetes Engine (GKE) for managing GPU/TPU workloads , autoscaling, and scheduling.
IaC & Automation: Mastery of Terraform to provision secure, complex GCP environments, IAM roles, and Vertex AI resources.
Key Responsibilities-
AI Solution Deployment : Deploy, fine-tune, and optimize large-scale Gen AI models and LLM orchestration frameworks within customer cloud environments.
Infrastructure Engineering : Architect scalable infrastructure for AI workloads utilizing GPU/TPU orchestration, high-performance storage, and low-latency networking.
Data & Retrieval Pipelines : Design and implement high-throughput data ingestion pipelines and Vector Database architectures for Retrieval-Augmented Generation (RAG).
Technical Advocacy : Act as the primary technical consultant, guiding enterprise clients through AI safety, prompt engineering patterns, and inference cost optimization.
Product Collaboration : Feed edge-case deployment insights back to core AI research and platform engineering teams to improve product robustness.
Soft Skills-
AI Consultation : Ability to manage customer expectations around LLM non-determinism, hallucinations, and performance trade-offs.
Rapid Adaptability : Passion for keeping pace with the weekly advancements in the Generative AI landscape.
Critical Debugging : Exceptional skill in isolating errors across complex software layers, from GPU drivers up to prompt engineering logic.
Mobility : Willingness to travel to client sites to lead high-stakes, on-site deployment sprints.
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.

Nice-to-haves

Programming: Strong coding skills in Python (preferred) or Go , with an emphasis on writing clean, concurrent code and utilizing the Google Cloud SDK .

Application prep

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Keywords to mirror

aimachine learninggcpllmsqlkubernetespythongo

Platform: workable
Effort: medium

Benefits

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